Bottom Line:
The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience.Manually optimizing CSG parameters turns out to be a counterintuitive task.Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations.

Purpose: Computer-assisted orthopedic surgery aims at minimizing invasiveness, postoperative pain, and morbidity with computer-assisted preoperative planning and intra-operative guidance techniques, of which camera-based navigation and patient-specific templates (PST) are the most common. PSTs are one-time templates that guide the surgeon initially in cutting slits or drilling holes. This method can be extended to reusable and customizable surgical guides (CSG), which can be adapted to the patients' bone. Determining the right set of CSG input parameters by hand is a challenging task, given the vast amount of input parameter combinations and the complex physical interaction between the PST/CSG and the bone.

Methods: This paper introduces a novel algorithm to solve the problem of choosing the right set of input parameters. Our approach predicts how well a CSG instance is able to reproduce the planned alignment based on a physical simulation and uses a genetic optimization algorithm to determine optimal configurations. We validate our technique with a prototype of a pin-based CSG and nine rapid prototyped distal femora.

Results: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience. Using the optimization technique, the alignment errors remained within practical boundaries of 1.2 mm translation and [Formula: see text] rotation error. In all cases, the proposed method outperformed manual optimization.

Conclusions: Manually optimizing CSG parameters turns out to be a counterintuitive task. Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations. Our optimization algorithm ensures that the CSG is configured correctly, and we could demonstrate that the intended alignment of the CSG is accurately reproduced on all tested bone geometries.

Fig8: example of a CSG pin configuration using a Poisson distribution. pin distribution as a result of random sampling, which leads to clumping of pins (exaggerated case). Although this pin distribution might work, there is a high probability that it will have a high alignment error, since there are no pins in the lower left corner

Mentions:
In our context, individuals correspond to different CSG configurations (see Fig. 2). In our case, each configuration csg consists of a set of active pins in the CSG (see Fig. 8). Initially, the CSG population consists of random active-pin distributions, which are established via a Poisson distribution to ensure a minimum distance between the pins and to avoid clumping, which leads to individuals with high alignment error that are unlikely to survive the genetic optimization. The pin insertion depth is determined automatically by moving the pins downward from the intended rest pose of the CSG until they collide with the surface of the bone.

Fig8: example of a CSG pin configuration using a Poisson distribution. pin distribution as a result of random sampling, which leads to clumping of pins (exaggerated case). Although this pin distribution might work, there is a high probability that it will have a high alignment error, since there are no pins in the lower left corner

Mentions:
In our context, individuals correspond to different CSG configurations (see Fig. 2). In our case, each configuration csg consists of a set of active pins in the CSG (see Fig. 8). Initially, the CSG population consists of random active-pin distributions, which are established via a Poisson distribution to ensure a minimum distance between the pins and to avoid clumping, which leads to individuals with high alignment error that are unlikely to survive the genetic optimization. The pin insertion depth is determined automatically by moving the pins downward from the intended rest pose of the CSG until they collide with the surface of the bone.

Bottom Line:
The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience.Manually optimizing CSG parameters turns out to be a counterintuitive task.Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations.

Purpose: Computer-assisted orthopedic surgery aims at minimizing invasiveness, postoperative pain, and morbidity with computer-assisted preoperative planning and intra-operative guidance techniques, of which camera-based navigation and patient-specific templates (PST) are the most common. PSTs are one-time templates that guide the surgeon initially in cutting slits or drilling holes. This method can be extended to reusable and customizable surgical guides (CSG), which can be adapted to the patients' bone. Determining the right set of CSG input parameters by hand is a challenging task, given the vast amount of input parameter combinations and the complex physical interaction between the PST/CSG and the bone.

Methods: This paper introduces a novel algorithm to solve the problem of choosing the right set of input parameters. Our approach predicts how well a CSG instance is able to reproduce the planned alignment based on a physical simulation and uses a genetic optimization algorithm to determine optimal configurations. We validate our technique with a prototype of a pin-based CSG and nine rapid prototyped distal femora.

Results: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience. Using the optimization technique, the alignment errors remained within practical boundaries of 1.2 mm translation and [Formula: see text] rotation error. In all cases, the proposed method outperformed manual optimization.

Conclusions: Manually optimizing CSG parameters turns out to be a counterintuitive task. Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations. Our optimization algorithm ensures that the CSG is configured correctly, and we could demonstrate that the intended alignment of the CSG is accurately reproduced on all tested bone geometries.